Deep Learning for Big Data Applications
Typically, training deep neural networks requires large amounts of data that often do not fit in memory. You do not need multiple computers to solve problems using data sets too large to fit in memory. Instead, you can divide your training data into mini-batches that contain a portion of the data set. By iterating over the mini-batches, networks can learn from large data sets without needing to load all data into memory at once. If your data is too large to fit in memory, use a data store to work with mini-batches of data for training and inference. MATLAB® provides many different types of data store tailored for different applications. For more information about data stores for different applications, see Data stores for Deep Learning. augmentedImageDatastore is specifically designed to pre-process and augment batches of image data for machine learning and computer vision applications.
Related Conference of Deep Learning for Big Data Applications
10th World Congress on Computer Science, Machine Learning and Big Data
10th International Conference and Expo on Computer Graphics & Animation
11th International Conference on Data Science and Machine Learning Applications
Deep Learning for Big Data Applications Conference Speakers
Recommended Sessions
- Big Data Analytics in Finance and Banking
- Big Data Security and Privacy
- Big Data Technologies and Tools session
- Case studies and best practices in Big Data Analytics
- Clustering and Association Rule Mining
- Data Cleaning and Preprocessing
- Deep Learning for Big Data Applications
- Exploratory Data Analysis (EDA)
- Foundations of Big Data Analysis
- Future Trends in Big Data Analysis and Data Mining
- Graph Mining and Network Analysis
- Machine Learning for Big Data
- Privacy-Preserving Data Mining
- Real Time Big Data Processing
- Recommender Systems and Personalization
- Social Network Analysis
- Stream Data Mining and Sensor Data Analysis
- Text Mining and Natural Language Processing
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